Biology subjects

Makkonen, M.

Publications and source records attributed to Makkonen, M..

4 recordsLinked to original sources

bioRxiv2026

Automated robotic control system for EEG-BCI-guided closed-loop TMS

Brain-state-guided and closed-loop transcranial magnetic stimulation (TMS) protocols have emerged as methods for decreasing the variability and increasing the therapeutic effectiveness of stimulation protocols. However, most existing brain-state-dependent TMS systems only control the timing of stimulation, while the location is fixed and manually adjusted between blocks or sessions. This limits flexible targeting of distributed networks. We developed a system that jointly manages TMS pulse timing and location automatically controlled by an electroencephalography (EEG)-based brain- computer interface (BCI). A machine-learning algorithm infers the brain state in real time to guide the robotic coil placement and target. We present a proof-of-concept study in which a BCI controlled both the target site and the timing of TMS. A pre-trained convolutional neural network discriminated between resting state and movements performed with the right or left hand; the classifier output determined the hemisphere in which primary-motor-cortex hand area was stimulated and when. Preprocessing and decoding of 2-s EEG segments required 150 ms, and the robot took 7.5 s to move from the vertex home position to the predefined motor targets. The EEG-BCI-guided robotic TMS system expands the toolkit for brain-state-dependent and closed-loop neurostimulation by enabling control of stimulus location based on volitional brain activity. Thus, the system can benefit both neuroscience research and clinical neuromodulation applications. A prominent application of the system is automatically controlling spinal cord injury or motor disorder TMS rehabilitation with motor imagery, optimizing stimulation timing to the brain state producing optimal rehabilitation results.

neuroscience↗

bioRxiv2026

TMS timed to interictal epileptiform discharges

Interictal epileptiform discharges (IEDs) are pathological hypersynchronous bursts of electrical brain activity that occur between seizures in patients with epilepsy. IEDs are caused by transient brain states that are difficult to predict, making them a challenging neurophysiological and technological case for brain-state-dependent stimulation. Administering stimulation at IED onset may provide insight into the epileptic network and optimize neurostimulation therapies. Here, we assessed the feasibility of IED-triggered transcranial magnetic stimulation (TMS) in two children with self-limited epilepsy with centrotemporal spikes (SeLECTS), a common pediatric epilepsy in which IEDs emerge from the motor cortex. A convolutional neural network (CNN) was trained on the participants pre-recorded electroencephalography (EEG) data with IEDs annotated by an epileptologist. The CNN was integrated into an EEG-processing pipeline that classified EEG segments as "IED" or "non-IED" in real time. With this pipeline, TMS pulses were administered during IED or non-IED periods in an interleaved, randomized design. We stimulated both the motor cortex generating the IEDs and the contralateral motor cortex and tested the impact of IEDs on TMS-evoked potentials (TEPs). Our study demonstrated that TMS can be timed to IEDs and that there is a site-specific increase in TEP amplitude when stimulating during IEDs. Out of the TMS pulses aimed at an IED, 39% and 19% were successfully delivered during an IED for the two participants, respectively. For future research, we propose ways to address the methodological challenges of IED-timed TMS, enabling brain-state-dependent TMS for epilepsy research and treatment.

neuroscience↗

bioRxiv2025

Predictive modeling of TMS-evoked responses: Unraveling instantaneous excitability states

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) and electromyography (EMG) provides a unique window into instantaneous cortical and corticospinal excitability states. We investigated 50 healthy participants to determine how fluctuations in pre-stimulus brain activity influence single-trial TMS-evoked potentials (TEPs) and motor-evoked potentials (MEPs). We developed a novel automated source-level TEP extraction method using individualized spatiotemporal priors that is robust against poor single-trial signal-to-noise ratios (SNRs) and ongoing oscillations. TEP and MEP amplitudes were predicted with linear mixed-effects models based on pre-stimulation EEG band-powers (theta to gamma), while accounting for temporal drifts (within-session trends), coil control, and inter-subject differences. We found that higher pre-stimulus sensorimotor alpha, beta, and gamma power were each associated with larger TEPs, indicating a more excitable cortical state. Increases in alpha and gamma power immediately before stimulation specifically predicted larger MEPs, reflecting increased corticospinal excitability. These results reveal relationships between ongoing oscillatory brain states and TMS response amplitudes, identifying EEG biomarkers of high- and low-excitability states. In conclusion, our study demonstrates the feasibility of single-trial source-level TMS-EEG analysis and shows that spontaneous alpha-, beta-, and gamma-band oscillations modulate motor cortical and corticospinal responsiveness. These findings pave the way for EEG-informed, brain-state-dependent TMS protocols to optimize neuromodulatory interventions in clinical and research applications.

neuroscience↗

bioRxiv2025

NeuroSimo: an open-source software for closed-loop EEG- or EMG-guided TMS

ObjectiveOur goal was to create open-source software for closed-loop EEG-TMS that allows researchers to rapidly prototype and develop novel stimulation paradigms in a high-level programming language. This addresses the limitations of current solutions, which often rely on proprietary hardware and software, limiting their accessibility and customizability, or comprise ad-hoc pipelines tailored to specific use cases. ApproachWe developed NeuroSimo, a software platform that enables arbitrary EEG-TMS stimulation protocols written in Python, leveraging Pythons ecosystem of scientific, neuroimaging, and machine learning libraries. The core software is written in C++ with an embedded Python interpreter and employs the Robot Operating System (ROS 2) for inter-process communication. NeuroSimo runs on real-time-enabled Linux Ubuntu, using LabJack T4 for pulse triggering, and supports two EEG device models (Bittium NeurOne, BrainProducts actiCHamp) and TMS devices that deliver pulses via trigger signals. The software includes a graphical user interface for configuration and performance monitoring, and supports GPU processing for neural network computations. Main resultsIn brain-state-dependent stimulation using the Phastimate algorithm, which targets TMS pulses to the trough of sensorimotor -rhythm, NeuroSimo achieved a median timing error of 0.2 ms (95% CI: 0.2-0.2 ms), a 99th-percentile of 0.6 ms (0.6-0.6 ms), and a maximum of 1.4 ms. SignificanceAs an open-source platform combining Pythons flexibility with real-time closed-loop EEG-TMS, NeuroSimo enables researchers to develop and implement novel therapeutic approaches, marking a significant advance in personalized brain stimulation.

bioengineering↗